The effect of steep Trendelenburg positioning on intraocular pressure and visual function during robotic-assisted radical prostatectomy.

BACKGROUND: To evaluate intraocular pressure (IOP) changes in patients undergoing robotic-assisted radical prostatectomy and to evaluate complications from increased IOP. METHODS: Thirty-one eyes scheduled for robotic prostatectomy were included. Perioperative IOP measurements were performed as follows: prior to induction of anaesthesia while supine and awake (T1); immediately post-induction while supine (T2); every hour from 0 to 5 h while anaesthetised in a steep Trendelenburg position (T3-T8); prior to awakening while supine (T9); and 30 min after awakening while supine (T10). A complete ophthalmic examination including visual acuity and retinal nerve fibre layer thickness (RNFL) was performed at enrolment and 1 month postoperatively. RESULTS: Average IOP (mm Hg) for each time point was as follows: T1=18.0, T2=9.8, T3=18.9, T4=21.6, T5=22.5, T6=22.3, T7=24.2, T8=24.0, T9=15.7 and T10=17.9. The proportion of eyes with intraoperative IOP ≧30 mm Hg were as follows: T3=0%, T4=3.23%, T5=9.68%, T6=6.45%, T7=22.22%, and T8=25%. Maximum IOP was 36 mm Hg. Mean visual acuity (logarithm of the minimal angle of resolution) and RNFL showed no statistically significant difference before and after operation and no other ocular complications were found at final examination. CONCLUSIONS: While IOP increased in a time-dependent fashion in anesthaetised patients undergoing robotic-assisted radical prostatectomy in a steep Trendelenburg position, visual function showed no significant change postoperatively and no complications were seen. Steep Trendelenburg positioning during time-limited procedures appears to pose little or no risk from IOP increases in patients without pre-existing ocular disease.

[Assessment of visual acuity in patients with age-related macular degeneration 1 year after photodynamic therapy].

PURPOSE: To evaluate visual acuity in patients who underwent photodynamic therapy (PDT) for exudative age-related macular degeneration (AMD). METHODS: 138 eyes undergoing PDT were studied retrospectively. 101 eyes with AMD (AMD group) and 37 eyes with polypoidal choroidal vasculopathy (PCV group) were evaluated. RESULTS: In the AMD group, one year after PDT, the log MAR visual acuity improved by 2 lines or more in 23 eyes (22.8%), and decreased by 2 lines or more in 27 eyes (26.7%). In the PCV group, one year after PDT the log MAR visual acuity improved by 2 lines or more in 13 eyes (35.1%), and decreased by 2 lines or more in 8 eyes (21.6%). Out of the 138 eyes, 30 eyes showed improvement in visual acuity by 0.6 or more, one year after PDT (AMD group : 17 eyes; PCV group: 13 eyes) CONCLUSIONS: Patients with PCV who had good primary visual acuity improved their visual acuity by 0.6 or more one year after PDT.

Optimal region-of-interest settings for tissue characterization based on ultrasonic elasticity imaging.

Pathologic changes in arterial walls significantly influence their mechanical properties. We have developed a correlation-based method, the phased tracking method, for measurement of the regional elasticity of the arterial wall. Using this method, elasticity distributions of lipids, blood clots, fibrous tissue and calcified tissue were measured by in-vitro experiments of excised arteries (mean +/- SD: lipids, 89 +/- 47 kPa; blood clots, 131 +/- 56 kPa; fibrous tissue, 1022 +/- 1040 kPa; calcified tissue, 2267 +/- 1228 kPa). It was found that arterial tissues can be classified into soft tissues (lipids and blood clots) and hard tissues (fibrous tissue and calcified tissue) on the basis of their elasticity. However, there are large overlaps between elasticity distributions of lipids and blood clots and those of fibrous tissue and calcified tissue. Thus, it was difficult to differentiate lipids from blood clots and fibrous tissue from calcified tissue by setting a threshold for a single elasticity value. Therefore, we previously proposed a tissue classification method using the elasticity distribution in each small region. In this method, the elasticity distribution of each small region of interest (ROI) (not a single pixel) in an elasticity image is used to classify lipids, blood clots, fibrous tissue and calcified tissue by calculating the likelihood function for each tissue. In the present study, the optimum size of the ROI and threshold T(o) for the likelihood function were investigated to improve the tissue classification. The ratio of correctly classified pixels to the total number of classified pixels was 29.8% when the size of a small region was 75 microm x 300 microm (a single pixel). The ratio of correctly classified pixels became 35.1% when the size of a small region was 1,500 microm x 1,500 microm (100 pixels). Moreover, a region with an extremely low likelihood with respect to all tissue components was defined as an unclassified region by setting threshold T(o) for the likelihood function to 0.21. The tissue classification of the arterial wall was improved using the elasticity distribution of a small region whose size was larger than the spatial resolution (800 microm x 600 microm) of ultrasound. In this study, the arteries used in construction of the elasticity databases were classified into each tissue using the constructed elasticity databases. Other arteries, which are not used for constructing the elasticity databases, should be classified in future work to thoroughly show the effectiveness of the proposed method.